Real-Time Face Tracking in a Digital Image Acquisition Device

ABSTRACT

An image processing apparatus for tracking faces in an image stream iteratively receives an acquired image from the image stream including one or more face regions. The acquired image is sub-sampled at a specified resolution to provide a sub-sampled image. An integral image is then calculated for a least a portion of the sub-sampled image. Fixed size face detection is applied to at least a portion of the integral image to provide a set of candidate face regions. Responsive to the set of candidate face regions produced and any previously detected candidate face regions, the resolution is adjusted for sub-sampling a subsequent acquired image.

CLAIM OF PRIORITY AND CROSS-REFERENCE TO RELATED APPLICATIONS

This application is a Division of U.S. patent application Ser. No.13/245,738, filed Sep. 26, 2011; which is a Continuation of U.S. patentapplication Ser. No. 12/063,089, filed Feb. 6, 2008, now U.S. Pat. No.8,055,029; which is a 371 of PCT Application Ser. No. PCT/EP2007/005330,filed Jun. 18, 2007; which claims the benefit of priority to U.S. patentapplication Ser. No. 11/464,083, filed Aug. 11, 2006, now U.S. Pat. No.7,315,631.

FIELD OF THE INVENTION

The present invention provides an improved method and apparatus forimage processing in acquisition devices. In particular the inventionprovides improved real-time face tracking in a digital image acquisitiondevice.

DESCRIPTION OF THE RELATED ART

Face tracking for digital image acquisition devices include methods ofmarking human faces in a series of images such as a video stream or acamera preview. Face tracking can be used to indicate to a photographerlocations of faces in an image, thereby improving acquisitionparameters, or allowing post processing of the images based on knowledgeof the locations of the faces.

In general, face tracking systems employ two principle modules: (i) adetection module for locating new candidate face regions in an acquiredimage or a sequence of images; and (ii) a tracking module for confirmingface regions.

A well-known fast-face detection algorithm is disclosed in US2002/0102024, hereinafter Viola-Jones, which is hereby incorporated byreference. In brief, Viola-Jones first derives an integral image from anacquired image, which is usually an image frame in a video stream. Eachelement of the integral image is calculated as the sum of intensities ofall points above and to the left of the point in the image. The totalintensity of any sub-window in an image can then be derived bysubtracting the integral image value for the top left point of thesub-window from the integral image value for the bottom right point ofthe sub-window. Also, intensities for adjacent sub-windows can beefficiently compared using particular combinations of integral imagevalues from points of the sub-windows.

In Viola-Jones, a chain (cascade) of 32 classifiers based on rectangular(and increasingly refined) Haar features are used with the integralimage by applying the classifiers to a sub-window within the integralimage. For a complete analysis of an acquired image, this sub-window isshifted incrementally across the integral image until the entire imagehas been covered.

In addition to moving the sub-window across the entire integral image,the sub window is also scaled up/down to cover the possible range offace sizes. In Viola-Jones, a scaling factor of 1.25 is used and,typically, a range of about 10-12 different scales are used to cover thepossible face sizes in an XVGA size image.

It will therefore be seen that the resolution of the integral image isdetermined by the smallest sized classifier sub-window, i.e. thesmallest size face to be detected, as larger sized sub-windows can useintermediate points within the integral image for their calculations.

A number of variants of the original Viola-Jones algorithm are known inthe literature. These generally employ rectangular, Haar featureclassifiers and use the integral image techniques of Viola-Jones.

Even though Viola-Jones is significantly faster than previous facedetectors, it still involves significant computation and a Pentium-classcomputer can only just about achieve real-time performance. In aresource-restricted embedded system, such as a hand held imageacquisition device, e.g., a digital camera, a hand-held computer or acellular phone equipped with a camera, it is generally not practical torun such a face detector at real-time frame rates for video. From testswithin a typical digital camera, it is possible to achieve completecoverage of all 10-12 sub-window scales with a 3-4 classifier cascade.This allows some level of initial face detection to be achieved, butwith undesirably high false positive rates.

In US 2005/0147278, by Rui et al., which is hereby incorporated byreference, a system is described for automatic detection and tracking ofmultiple individuals using multiple cues. Rui et al. disclose usingViola-Jones as a fast face detector. However, in order to avoid theprocessing overhead of Viola-Jones, Rui et al. instead disclose using anauto-initialization module which uses a combination of motion, audio andfast face detection to detect new faces in the frame of a videosequence. The remainder of the system employs well-known face trackingmethods to follow existing or newly discovered candidate face regionsfrom frame to frame. It is also noted that Rui et al. involves somevideo frames being dropped in order to run a complete face detectionprocess.

US 2006/00029265, Kim and U.S. Pat. No. 7,190,829, Zhang each disclosureapplying a skin color filter to an acquired image to produce a skin mapon which face detection is subsequently performed to restrict theprocessing required to perform face detection and tracking.

SUMMARY OF THE INVENTION

Methods are provided for detecting, tracking or recognizing faces, orcombinations thereof, within acquired digital images of an image stream.An image processing apparatus is also provided including one or moreprocessors and one or more digital storage media havingdigitally-encoded instructions embedded therein for programming the oneor more processors to perform any of these methods.

A first method is provided for detecting faces in an image stream usinga digital image acquisition device. An acquired image is received froman image stream including one or more face regions. An acquired image issub-sampled at a specified resolution to provide a sub-sampled image.One or more regions of said acquired image are identified thatpredominantly include skin tones. A corresponding integral image iscalculated for a least one of the skin tone regions of the sub-sampledacquired image. Face detection is applied to at least a portion of theintegral image to provide a set of one or more candidate face regionseach having a given size and a respective location.

By only running the face detector on regions predominantly includingskin tones, more relaxed face detection can be used, as there is ahigher chance that these skin-tone regions do in fact contain a face.So, faster face detection can be employed to more effectively providesimilar quality results to running face detection over the whole imagewith stricter face detection involved in positively detecting a face.

BRIEF DESCRIPTION OF THE DRAWINGS

Embodiments of the invention will now be described by way of example,with reference to the accompanying drawings, in which:

FIG. 1 is a block diagram illustrating principle components of an imageprocessing apparatus in accordance with a preferred embodiment;

FIG. 2 is a flow diagram illustrating operation of the image processingapparatus of FIG. 1;

FIGS. 3( a) to 3(d) illustrate examples of images processed by anapparatus in accordance with a preferred embodiment;

FIGS. 4( a) and 4(b) illustrate skin detection functions and contrastenhancement functions respectively for use in an embodiment of theinvention; and

FIG. 5 shows a flow diagram for acquiring a face in an embodiment of theinvention.

DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

FIG. 1 shows the primary subsystems of a face tracking system inaccordance with a preferred embodiment. The solid lines indicate theflow of image data; the dashed line indicate control inputs orinformation outputs (e.g. location(s) of detected faces) from a module.In this example an image processing apparatus can be a digital stillcamera (DSC), a video camera, a cell phone equipped with an imagecapturing mechanism or a hand help computer equipped with an internal orexternal camera, or a combination thereof.

A digital image is acquired in raw format from an image sensor (CCD orCMOS) [105] and an image subsampler [112] generates a smaller copy ofthe main image. Most digital cameras already contain dedicated hardwaresubsystems to perform image subsampling, for example to provide previewimages to a camera display. Typically, the subsampled image is providedin bitmap format (RGB or YCC). In the meantime, the normal imageacquisition chain performs post-processing on the raw image [110] whichtypically includes some luminance and color balancing. In certaindigital imaging systems the subsampling may occur after suchpost-processing, or after certain post-processing filters are applied,but before the entire post-processing filter chain is completed.

The subsampled image is next passed to an integral image generator [115]which creates an integral image from the subsampled image. This integralimage is next passed to a fixed size face detector [120]. The facedetector is applied to the full integral image, but as this is anintegral image of a subsampled copy of the main image, the processinginvolved in the face detection is proportionately reduced. If thesubsampled image is ¼ of the main image, e.g., has ¼ the number ofpixels and/or ¼ the size, then the processing time involved is onlyabout 25% of that for the full image.

This approach is particularly amenable to hardware embodiments where thesubsampled image memory space can be scanned by a fixed size DMA windowand digital logic to implement a Haar-feature classifier chain can beapplied to this DMA window. Several sizes of classifiers mayalternatively be used (in a software embodiment), or multiple fixed-sizeclassifiers may be used (in a hardware embodiment). An advantage is thata smaller integral image is calculated.

After application of the fast face detector [280] any newly detectedcandidate face regions are passed onto a face tracking module [111]where any face regions confirmed from previous analysis [145] may bemerged with new candidate face regions prior to being provided [142] toa face tracker [290].

The face tracker [290] provides a set of confirmed candidate regions[143] back to the tracking module [111]. Additional image processingfilters are preferably applied by the tracking module [111] to confirmeither that these confirmed regions [143] are face regions or tomaintain regions as candidates if they have not been confirmed as suchby the face tracker [290]. A final set of face regions [145] can beoutput by the module [111] for use elsewhere in the camera or to bestored within or in association with an acquired image for laterprocessing either within the camera or offline; as well as to be used ina next iteration of face tracking.

After the main image acquisition chain is completed a full-size copy ofthe main image will normally reside in the system memory [140] of theimage acquisition system. This may be accessed by a candidate regionextractor [125] component of the face tracker [290] which selects imagepatches based on candidate face region data [142] obtained from the facetracking module [111]. These image patches for each candidate region arepassed to an integral image generator [115] which passes the resultingintegral images to a variable sized detector [121], as one possibleexample a VJ detector, which then applies a classifier chain, preferablyat least a 32 classifier chain, to the integral image for each candidateregion across a range of different scales.

The range of scales [144] employed by the face detector [121] isdetermined and supplied by the face tracking module [111] and is basedpartly on statistical information relating to the history of the currentcandidate face regions [142] and partly on external metadata determinedfrom other subsystems within the image acquisition system.

As an example of the former, if a candidate face region has remainedconsistently at a particular size for a certain number of acquired imageframes then the face detector [121] is applied at this particular scaleand/or perhaps at one scale higher (i.e. 1.25 time larger) and one scalelower (i.e. 1.25 times lower).

As an example of the latter, if the focus of the image acquisitionsystem has moved to approximately infinity, then the smallest scalingswill be applied in the face detector [121]. Normally these scalingswould not be employed as they would be applied a greater number of timesto the candidate face region in order to cover it completely. It isworthwhile noting that the candidate face region will have a minimumsize beyond which it should not decrease—this is in order to allow forlocalized movement of the camera by a user between frames. In some imageacquisition systems which contain motion sensors, such localizedmovements may be tracked. This information may be employed to furtherimprove the selection of scales and the size of candidate regions.

The candidate region tracker [290] provides a set of confirmed faceregions [143] based on full variable size face detection of the imagepatches to the face tracking module [111]. Clearly, some candidateregions will have been confirmed while others will have been rejected,and these can be explicitly returned by the tracker [290] or can becalculated by the tracking module [111] by analyzing the differencebetween the confirmed regions [143] and the candidate regions [142]. Ineither case, the face tracking module [111] can then apply alternativetests to candidate regions rejected by the tracker [290] (as explainedbelow) to determine whether these should be maintained as candidateregions [142] for the next cycle of tracking or whether these shouldindeed be removed from tracking.

Once the set of confirmed candidate regions [145] has been determined bythe face tracking module [111], the module [111] communicates with thesub-sampler [112] to determine when the next acquired image is to besub-sampled, and so provided to the detector [280], and also to providethe resolution [146] at which the next acquired image is to besub-sampled.

Where the detector [280] does not run when the next image is acquired,the candidate regions [142] provided to the extractor [125] for the nextacquired image will be the regions [145] confirmed by the trackingmodule [111] from the last acquired image. On the other hand, when theface detector [280] provides a new set of candidate regions [141] to theface tracking module [111], these candidate regions are preferablymerged with the previous set of confirmed regions [145] to provide theset of candidate regions [142] to the extractor [125] for the nextacquired image.

It will be appreciated that, as described in co-pending application No.60/892,883 filed Mar. 5, 2007 (Ref: FN182), in face detection processessuch as disclosed in Violla-Jones, during analysis of a detection windowand/or while oscillating around the detection window, a confidence levelcan be accumulated providing a probabilistic measure of a face beingpresent at the location of the detection window. When the confidencelevel reaches a preset threshold for a detection window, a face isconfirmed for the location of the detection window. Where a facedetection process generates such a confidence level for a given locationof detection window, the confidence level can be captured and stored asan indicator of the probability of a face existing at the givenlocation, even if a face is not detected.

Alternatively, where a face detection process applies a sequence oftests each of which produce a Boolean “Face” or “No face” result, theextent to which the face detection process has progressed through thesequence before deciding no face exists at the location can be taken asequivalent to a confidence level and indicating the probability of aface existing at the given location. So for example, where a cascade ofclassifiers failed to detect a face at a window location at classifier20 of 32, it could be taken that this location is more likely to includea face (possibly at a different scale or shifted slightly) than where acascade of classifiers failed to detect a face at a window location atclassifier 10 of 32.

Thus, when using real-time face tracking within a camera, on each frameof the preview stream, a current probability for each face region can beavailable, together with a cumulative probability which is determinedfrom a history of each face region across the previous N preview images.In normal situations the cumulative probability is relatively high, say70%+, and the current probability would be of the same order with anerror factor of, say −10%. This information can be used in refinedembodiments of the invention explained in more detail below to optimizethe processing overhead required by face detection/tracking.

Zoom information may be obtained from camera firmware. Using softwaretechniques which analyze images in camera memory 140 or image store 150,the degree of pan or tilt of the camera may be determined from one imageto another.

In one embodiment, the acquisition device is provided with a motionsensor 180, as illustrated at FIG. 1, to determine the degree anddirection of pan from one image to another, and avoiding the processinginvolved in determining camera movement in software.

Such motion sensor for a digital camera may be based on anaccelerometer, and may be optionally based on gyroscopic principalswithin the camera, primarily for the purposes of warning or compensatingfor hand shake during main image capture. U.S. Pat. No. 4,448,510, toMurakoshi, which is hereby incorporated by reference, discloses such asystem for a conventional camera, and U.S. Pat. No. 6,747,690, toMolgaard, which is also incorporated by reference, disclosesaccelerometer sensors applied within a modern digital camera.

Where a motion sensor is incorporated in a camera, it may be optimizedfor small movements around the optical axis. The accelerometer mayincorporate a sensing module which generates a signal based on theacceleration experienced and an amplifier module which determines therange of accelerations which can effectively be measured. Theaccelerometer may allow software control of the amplifier stage whichallows the sensitivity to be adjusted.

The motion sensor 180 could equally be implemented with MEMS sensors ofthe sort which will be incorporated in next generation consumer camerasand camera-phones.

In any case, when the camera is operable in face tracking mode, i.e.constant video acquisition as distinct from acquiring a main image,shake compensation would typically not be used because image quality islower. This provides the opportunity to configure the motion sensor 180to sense large movements by setting the motion sensor amplifier moduleto low gain. The size and direction of movement detected by the sensor180 is preferably provided to the face tracker 111. The approximate sizeof faces being tracked is already known, and this enables an estimate ofthe distance of each face from the camera. Accordingly, knowing theapproximate size of the large movement from the sensor 180 allows theapproximate displacement of each candidate face region to be determined,even if they are at differing distances from the camera.

Thus, when a large movement is detected, the face tracker 111 shifts thelocations of candidate regions as a function of the direction and sizeof the movement. Alternatively, the size of the region over which thetracking algorithms are applied may also be enlarged (and thesophistication of the tracker may be decreased to compensate forscanning a larger image area) as a function of the direction and size ofthe movement.

When the camera is actuated to capture a main image, or when it exitsface tracking mode for any other reason, the amplifier gain of themotion sensor 180 is returned to normal, allowing the main imageacquisition chain 105,110 for full-sized images to employ normal shakecompensation algorithms based on information from the motion sensor 180.

An alternative way of limiting the areas of an image to which the facedetector 120 is to be applied involves identifying areas of the imagewhich include skin tones. U.S. Pat. No. 6,661,907, which is herebyincorporated by reference, discloses one such technique for detectingskin tones and subsequently only applying face detection in regionshaving a predominant skin color.

In one embodiment, skin segmentation 190 is preferably applied to asub-sampled version of the acquired image. If the resolution of thesub-sampled version is not sufficient, then a previous image stored inimage store 150 or a next sub-sampled image can be used as long as thetwo images are not too different in content from the current acquiredimage. Alternatively, skin segmentation 190 can be applied to the fullsize video image 130.

In any case, regions containing skin tones are identified by boundingrectangles and these bounding rectangles are provided to the integralimage generator 115 which produces integral image patches correspondingto the rectangles in a manner similar to the tracker integral imagegenerator 115.

If acquired images are affected by external changes in the acquisitionconditions, bad illumination or incorrect exposure, the number of facesdetected or the current probability of a given face when compared withits historic cumulative probability could drop significantly even to thepoint where no faces in an image of a scene are detected. For example, aface may move from a region of frontal lighting into a region where isit subject to back-lighting, or side-lighting; or the overall lightingin a scene may suddenly be reduced (artificial lighting is turned off,or the scene moves from outdoors to indoors, etc).

In an attempt to avoid losing track of faces in a scene which might bedetected/tracked, where the skin segmentation module 190 detects eitherthat no candidate regions 145 are being tracked or thecumulative/current probability of candidate regions 145 is droppingsignificantly, the module 190 can adjust the criteria being applied todetect skin.

So, for example, in normal lighting conditions, skin segmentationcriteria such as disclosed in U.S. Ser. No. 11/624,683 filed Jan. 18,2007 (Ref: FN185) can be employed. So where image information isavailable in RGB format, if L>240, where L=0.3*R+0.59G+0.11B, or ifR>G+K and R>B+K where K is a function of image saturation, a pixel isdeemed to be skin. In YCC format, if Y>240 or ifCr>148.8162−0.1626*Cb+0.4726*K and Cr>1.2639*Cb−33.7803+0.7133*K, whereK is a function of image saturation, a pixel is deemed to be skin.

This produces the most limited skin map as illustrated in FIG. 4( a) asskin type A1 and so reduces the face detection processing overheadgreatly.

However, in a poorly illuminated image, the variations of which aredescribed in more detail later, a more relaxed skin criterion isemployed. For example, in RGB format, if R>G, a pixel is deemed to beskin. In YCC format, if Cr+0.1626*Cb−148.8162>0, a pixel is deemed to beskin.

This produces a more extensive skin map as illustrated in FIG. 4( a) asskin type A2 to allow face detection, possibly with the benefit ofcontrast enhancement described later, to pick up on any potential facecandidates.

Of course, for both skin types A1 and A2, other criteria can be employedfor RGB, YCC or other formats such as LAB etc.

Where an image is either very poorly illuminated, very over exposed,where there is wrong white balance, or where there is unusualillumination, for example, a colored bulb, no skin segmentation isperformed and all pixels are assumed to potentially include skin. Thisis referred to as skin type A3 and typically face detection is appliedto such an image in conjunction with some form of contrast enhancement.

Applying appropriate skin segmentation prior to face detection, notalone reduces the processing overhead associated with producing theintegral image and running face detection, but in the presentembodiment, it also allows the face detector 120 to apply more relaxedface detection to the bounding rectangles, as there is a higher chancethat these skin-tone regions do in fact contain a face. So for a VJdetector 120, a shorter classifier chain can be employed to moreeffectively provide similar quality results to running face detectionover the whole image with longer VJ classifiers required to positivelydetect a face.

Further improvements to face detection are also contemplated in otherembodiments. Again, based on the fact that face detection can be verydependent on illumination conditions, such that small variations inillumination can cause face detection to fail and cause somewhatunstable detection behavior, in another embodiment, confirmed faceregions 145 are used to identify regions of a subsequently acquiredsub-sampled image on which luminance correction [195] may be performedto bring regions of interest of the image to be analyzed to the desiredparameters. One example of such correction is to improve the luminancecontrast either across an entire image or within the regions of thesub-sampled image defined by confirmed face regions 145.

Contrast enhancement may be used to increase local contrast of an image,especially when the usable data of the image is represented by closecontrast values. Through this adjustment, intensities of pixels of aregion when represented on a histogram which would otherwise be closelydistributed can be better distributed. This allows for areas of lowerlocal contrast to gain a higher contrast without affecting globalcontrast. Histogram equalization accomplishes this by effectivelyspreading out the most frequent intensity values.

Where the luminance correction module 195 detects either that nocandidate regions 145 are being tracked or the cumulative/currentprobability of candidate regions 145 is dropping significantly or thatthe quality of the image or candidate regions of an acquired image isnot optimal, then contrast enhancement functions can be applied eitherto the entire image or the candidate regions prior to face detection.(If skin segmentation has been performed prior to luminance correction,then luminance correction need only be applied to regions of the imagedeemed to be skin.)

The contrast enhancement function can be implemented as a set of look uptables (LUT) applied to the image or candidate regions of the image forspecific image quality conditions. In one implementation, illuminationconditions can be determined from an analysis of luminance in ahistogram of the image or candidate region. If there is a clearindication of whether the image/region is low-lit, backlit orover-exposed, then a contrast enhancing function such as shown in FIG.4( b) to be applied by the correction module 195 can be selected andapplied as follows:

B.0.—no contrast enhancement lut(i)=iB.1.—for severe low-light conditions lut(i)=((L−1)*log(1+i))/log(L)B.2.—for medium low-light conditions lut(i)=((L−1)*pow(i/(L−1), 0.4))B.3.—for mild low-light conditions lut(i)=((L−1)*pow(i/(L−1), 0.5))B.4.—for severe overexposure conditionslut(i)=(L−1)*(exp(i)−1)/(exp(L−1)−1)B.5.—for medium overexposure conditions lut(i)=((L−1)*pow(i/(L−1), 1.4))B.6.—for mild overexposure conditions lut(i)=((L−1)*pow(i/(L−1), 1.6))B.7.—for a backlit image, which doesn't fall into the above categories,or for extreme lowlight/overexposed cases:

if i<=T then lut(i)=(T*pow(i/T, r))

-   -   else lut(i)=(L−1−(L−1−T)*pow((L−1−i)/(L−1−T),r))

where T=100, r=0.4; and

where L is the maximum value for luminance e.g. 256 for an 8-bit image.

It can therefore be seen that in the case of B2, B3, B5, B6, the generalformula for contrast enhancement is lut(i)=((L−1)*pow(i/(L−1), r)),where r<1 is used in lowlight, and r>1 is used in highlight conditions.

In the case above, the measure of quality is an assessment of therelative illumination of a candidate region of an image or indeed anentire image from a luminance histogram of the region/image. So forexample, a region/image can be categorized as subject to “severelow-light” if, excluding outlying samples, say the top 3%, the highest Yvalue of the region/image is less than 30. A region/image can becategorized as subject to “medium low-light” if, excluding outlyingsamples, say the top 3%, the highest Y value of the region/image is lessthan 50. A region/image can be categorized as subject to “severeoverexposure” if, excluding outlying samples, say the bottom 3%, thelowest Y value of the region/image is more than 220.

Another measure a quality is based on a combination of luminance andluminance variance. So if a tracked region has poor contrast i.e. a lowvariance in luminance and the face is subject to “severe low-light” asabove, contrast enhancement can be set to B.1. If a tracked regiondoesn't have good contrast and the face is subject to any form ofoverexposure, then contrast enhancement can be set to B.4. If thecontrast on the face is very good i.e. high variance in luminance,enhancement can be set to B.0.

If an intermediate level of contrast were detected, then either nochange to contrast enhancement would be made or the decision to changecontrast enhancement could be based completely on maximum/minimumluminance values.

In each case, an adjustment of the contrast enhancement function to takeinto account poor image quality can be accompanied by having the skinsegmentation module 190 switch from skin type A1 to A2 or from to skintype A1 or A2 to A3.

The method is useful in images with backgrounds and foregrounds that areboth bright or both dark. In particular, the method can lead to betterdetail in photographs that are over-exposed or under-exposed.

Alternatively, this luminance correction can be included in thecomputation of an “adjusted” integral image in the generators 115.

In another improvement, when face detection is being used, the cameraapplication is set to dynamically modify the exposure from the computeddefault to a higher values (from frame to frame, slightly overexposingthe scene) until the face detection provides a lock onto a face.

Further embodiments providing improved efficiency for the systemdescribed above are also contemplated. For example, face detectionalgorithms typically employ methods or use classifiers to detect facesin a picture at different orientations: 0, 90, 180 and 270 degrees. Thecamera may be equipped with an orientation sensor 170, as illustrated atFIG. 1. This can include a hardware sensor for determining whether thecamera is being held upright, inverted or tilted clockwise oranti-clockwise. Alternatively, the orientation sensor can comprise animage analysis module connected either to the image acquisition hardware105, 110 or camera memory 140 or image store 150 for quickly determiningwhether images are being acquired in portrait or landscape mode andwhether the camera is tilted clockwise or anti-clockwise.

Once this determination is made, the camera orientation can be fed toone or both of the face detectors 120, 121. The detectors may apply facedetection according to the likely orientation of faces in an imageacquired with the determined camera orientation. This feature can eithersignificantly reduce the face detection processing overhead, forexample, by avoiding the employment of classifiers which are unlikely todetect faces or increase its accuracy by running classifiers more likelyto detects faces in a given orientation more often.

FIG. 2 illustrates a main workflow in accordance with a preferredembodiment. The illustrated process is split into (i) adetection/initialization phase which finds new candidate face regions[141] using a fast face detector [280] which operates on a sub-sampledversion of the full image; (ii) a secondary face detection process [290]which operates on extracted image patches for candidate regions [142],which are determined based on locations of faces in one or morepreviously acquired image frames, and (iii) a main tracking processwhich computes and stores a statistical history of confirmed faceregions [143]. Although the application of the fast face detector [280]is shown occurring prior to the application of the candidate regiontracker [290] in FIG. 2, the order is not critical and the fastdetection is not necessarily executed on every frame or in certaincircumstances may be spread across multiple frames.

Thus, in step 205 the main image is acquired and in step 210 primaryimage processing of that main image is performed as described inrelation to FIG. 1. The sub-sampled image is generated by thesub-sampler [112] and an integral image is generated therefrom by thegenerator [115] at step 211. The integral image is passed to the fixedsize face detector [120] and the fixed size window provides a set ofcandidate face regions [141] within the integral image to the facetracking module step 220. The size of these regions is determined by thesub-sampling scale [146] specified by the face tracking module to thesub-sampler and this scale is preferably based on an analysis ofprevious sub-sampled/integral images by the detector [280] and patchesfrom previous acquired images by the tracker [290] as well perhaps asother inputs such as camera focus and movement.

The set of candidate regions [141] is merged with the existing set ofconfirmed regions [145] to produce a merged set of candidate regions[142] to be provided for confirmation at step 242.

For the candidate regions [142] specified by the face tracking module111, the candidate region extractor [125] extracts the correspondingfull resolution patches from an acquired image at step 225. An integralimage is generated for each extracted patch at step 230 and avariable-size face detection is applied by the face detector 121 to eachsuch integral image patch, for example, a full Viola-Jones analysis.These results [143] are in turn fed back to the face-tracking module[111] at step 240.

The tracking module [111] processes these regions [143] further before aset of confirmed regions [145] is output. In this regard, additionalfilters can be applied by the module 111 either for regions [143]confirmed by the tracker [290] or for retaining candidate regions [142]which may not have been confirmed by the tracker 290 or picked up by thedetector [280] at step 245. For example, if a face region had beentracked over a sequence of acquired images and then lost, a skinprototype could be applied to the region by the module [111] to check ifa subject facing the camera had just turned away. If so, this candidateregion may be maintained for checking in a next acquired image whetherthe subject turns back to face the camera.

Depending on the sizes of the confirmed regions being maintained at anygiven time and the history of their sizes, e.g. are they getting biggeror smaller, the module 111 determines the scale [146] for sub-samplingthe next acquired image to be analyzed by the detector [280] andprovides this to the sub-sampler [112] step 250.

The fast face detector [280] need not run on every acquired image. So,for example, where only a single source of sub-sampled images isavailable, if a camera acquires 60 frames per second, 15-25 sub-sampledframes per second (fps) may be required to be provided to the cameradisplay for user previewing. Clearly, these images need to besub-sampled at the same scale and at a high enough resolution for thedisplay. Some or all of the remaining 35-45 fps can be sampled at thescale required by the tracking module [111] for face detection andtracking purposes.

The decision on the periodicity in which images are being selected fromthe stream may be based on a fixed number or alternatively be a run-timevariable. In such cases, the decision on the next sampled image may bedetermined on the processing time it took for the previous image, inorder to maintain synchronicity between the captured real-time streamand the face tracking processing. Thus in a complex image environment,the sample rate may decrease.

Alternatively, the decision on the next sample may also be performedbased on processing of the content of selected images. If there is nosignificant change in the image stream, the full face tracking processmight not be performed. In such cases, although the sampling rate may beconstant, the images will undergo a simple image comparison and only ifit is decided that there is justifiable differences, will the facetracking algorithms be launched.

The face detector [280] also need not run at regular intervals. So forexample, if the camera focus is changed significantly, then the facedetector may be run more frequently and particularly with differingscales of sub-sampled images to try to detect faces which should bechanging in size. Alternatively, where focus is changing rapidly, thedetector [280] could be skipped for intervening frames, until focus hasstabilized. However, it is generally when focus goes to approximatelyinfinity that the highest resolution integral image is to be produced bythe generator [115].

In this latter case, the detector may not be able to cover the entirearea of the acquired, subsampled, image in a single frame. Accordinglythe detector may be applied across only a portion of the acquired,subsampled, image on a first frame, and across the remaining portion(s)of the image on one or more subsequent acquired image frames. In a oneembodiment, the detector is applied to the outer regions of the acquiredimage on a first acquired image frame in order to catch small facesentering the image from its periphery, and on subsequent frames to morecentral regions of the image.

In a separate embodiment, the face detector 120 will be applied only tothe regions that are substantively different between images. Note thatprior to comparing two sampled images for change in content, a stage ofregistration between the images may be needed to remove the variabilityof changes in camera, caused by camera movement such as zoom, pan andtilt.

In alternative embodiments, sub-sampled preview images for the cameradisplay can be fed through a separate pipe than the images being fed toand supplied from the image sub-sampler [112] and so every acquiredimage and its sub-sampled copies can be available both to the detector[280] as well as for camera display.

In addition to periodically acquiring samples from a video stream, theprocess may also be applied to a single still image acquired by adigital camera. In this case, the stream for the face tracking mayinclude a stream of preview images, and the final image in the seriesmay be the full resolution acquired image. In such a case, the facetracking information can be verified for the final image in a similarfashion to that described in FIG. 2. In addition, information such ascoordinates or mask of the face may be stored with the final image. Suchdata may fit as an entry in a saved image header, for example, forfuture post-processing, whether in the acquisition device or at a laterstage by an external device.

As mentioned above, to perform well, face detection and trackingrequires images with good contrast and color definition. In preferredembodiments of the present invention, as an alternative to or inaddition to correcting the luminance and contrast of candidate faceregions 145, the module 195 can adaptively change the contrast ofacquired images of a scene where no faces have been detected, i.e. nocandidate regions 145, such that adverse effects due to illumination,exposure or white balance are reduced.

As can be seen from FIGS. 4( a) and (b), there are many possiblepermutations of skin segmentation (A1/A2/A3) and contrast enhancement(B0/B1/B2/B3/B4/B5/B6/B7) which could be used to lock onto one or morefaces. If all of these were to be checked across a sequence of frames,then a long searching loop could be required and the lag for lockingonto a face may be unacceptably long.

Thus, in one embodiment, if there no candidate regions 145 from previousframes supplied from the face tracking module 111, the modules 190 and195 operate according to the steps illustrated in FIG. 5, to attempt todetect a face region.

Preferably, the modules 190,195 are biased towards searching with anormal skin map (A1) and without contrast enhancement (B0) and so themodules process sequences of frames in response to no candidate regionsbeing found in a frame as follows:

-   -   in a first 6 consecutive frames, the modules 190,195 use the        normal skin segmentation and contrast enhancement cases        (A.1+B.0);    -   in the next 3 consecutive frames, the modules use a relaxed case        (A.2+B.7);    -   in a next 6 consecutive frames, the modules again use the normal        case (A1+B.0); and    -   in the next 3 consecutive frames, the modules use a very-relaxed        case (A.3+B.7) before repeating the loop.

As can be seen, only few general cases (A.1+B.0; A.2+B.7; A.3+B.7) areemployed within this loop and if one of these combinations produces atleast one candidate face region, then the modules 190, 195 stopsearching and continue to use the successful A/B setting unlesscurrent/cumulative probability for candidate region(s) 145 being trackedor image quality changes. Then, either skin segmentation or contrastenhancement can be further changed to any one of functions A1 . . . A3or B0 . . . B7 in accordance with the illumination of a frame beinganalysed. Nonetheless, in the absence of a change in image illumination,using the setting produced from searching provides a better lock ondetected faces.

In spite of the modules 190,195 adjusting the skin segmentation andcontrast enhancement for candidate regions 145, if at any time the listof candidate regions 145 becomes empty again, the modules 190,195 beginsearching for the appropriate skin segmentation and contrast enhancementfunction in accordance with FIG. 5.

It should be noted that it is possible for a face to be in a normalcondition (good illumination, good contrast), and to be detected withrelaxed conditions such as A.2+B.7, for example, if the face justreturned from a profile. As such, it is always possible for either ofthe modules 190,195 to normalise contrast enhancement or tighten skinsegmentation, once the current/cumulative probability for tracked faceregions exceeds a given threshold or if the quality of the images interms of contrast, skin percentage and face illumination improves.

Skin percentage is counted as the number of pixels in an image regardedas normal skin, i.e. satisfying the A1 criteria, and the number regardedas relaxed skin, i.e. satisfying the A2 criteria.

If there is sufficient normal skin in a frame, then the skinsegmentation module can be set to skin type A1, whereas if there isinsufficient normal skin vis-à-vis relaxed skin on this face, the skinsegmentation module can be set to skin type A2 or even A3. This dynamicchange in skin segmentation can be necessary if a face changes itsorientation in plane or out of the plane, or if a camera's automaticwhite balancing (AWB) modifies the colors in acquiring a stream ofimages.

As an alternative or in addition to the above steps, when a face isdetected through the steps of FIG. 5, a check to determine if the mostappropriate skin segmentation is being used can be performed to ensurethe minimal amount of face detection is being carried out to track faceregions. So over the next two frames following detection of a face,either by cycling from A1->A2->A3 or from A3->A2->A1, skin segmentationcan be varied to restrict as much as possible the area to which facedetection is applied and still track detected faces.

The following are some exemplary scenarios indicating the changes inskin segmentation and contrast enhancement during acquisition of a faceregion:

Scenario 1—wrong WhiteBalance. . .

=>countFramesFromLastDetection=1

=>set the normal case (A.1+B.0)

. . .

=>countFramesFromLastDetection=7

=>set the case (A.2+B.7)

. . .

=>countFramesFromLastDetection=16

=>set the case (A.3+B.7)

=>detected one face

=>analyze the skin in the face region

=>normal skin percent is 0%, relaxed skin percent is 3%

=>A.3 will be used next frame . . .

=>analyze the luminance/contrast in the face region

=>good contrast (variance on the face>1500)

=>set the enhance B.0

. . .Scenario 2—normal skin, good illumination. . .

=>countFramesFromLastDetection=1

=>set the normal case (A.1+B.0)

=>detected one face

=>analyze the skin in the face region

=>normal skin percent is 80%, relaxed skin percent is 95%

=>A.1 will be used for the next frame . . .

=>analyze the luminance/contrast in the face region

=>good contrast (variance on the face>1500)

=>set the enhance B.0

. . .Scenario 3—backlight medium (relaxed skin type, and contrast enhancetype medium). . .

=>countFramesFromLastDetection=7

=>set the case (A.2+B.7)

=>one face detected

=>analyze the skin in the face region

=>normal skin percent is 30%, relaxed skin percent is 80%

=>A.2 will be used for the next frame . . .

=>analyze the luminance/contrast in the face region

=>maximum face histogram luminance<50

=>enhance contrast lowlight medium (B.2)

. . .Scenario 4—strong lowlight

=>countFramesFromLastDetection=1

=>set the normal case (A.1+B.0.)

=>maximum frame histogram luminance<30

=>enhance contrast lowlight strong (B.3), and set skin segmentation(A.3)

. . .

=>one face detected

=>analyze the skin in the face region

=>normal skin percent is 5%, relaxed skin percent is 15%

=>no contrast enhance modification

=>set skin segmentation to A.3

=>analyze the luminance/contrast in the face region

=>maximum face histogram luminance<30

=>enhance contrast lowlight strong (B.3)

. . .

=>countFramesFromLastDetection=0 (because we have a new detected face inthe list)

=>do nothing (because we have a new detected face in the list)

=>maximum frame histogram luminance<30

=>enhance contrast lowlight strong (B.3), and set skin segmentation(A.3)

=>re-detected the face

=>no new face detected

=>analyze the skin in the face region

=>normal skin percent is 5%, relaxed skin percent is 15%

=>no contrast enhance modification

=>set skin segmentation (A.3)

=>analyze the luminance/contrast in the face region

=>maximum face histogram luminance<30

=>enhance contrast lowlight strong (B.3)

. . .

FIG. 3 illustrates operation in accordance with a preferred embodimentthrough a more general worked example not dependent on skin segmentationand contrast enhancement. FIG. 3( a) illustrates a result at the end ofa detection and tracking cycle on a frame of video, with two confirmedface regions [301, 302] of different scales being shown. In thisexemplary embodiment, for pragmatic reasons, each face region has arectangular bounding box. Although it is easier to make computations onrectangular regions, different shapes can be used. This information isrecorded and output as [145] by the tracking module [111] of FIG. 1.

Based on a history of the face regions [301,302], the tracking module[111] may decide to run fast face tracking with a classifier window ofthe size of face region [301] with an integral image being provided andanalyzed accordingly.

FIG. 3( b) shows the situation after the next frame in a video sequenceis captured and the fast face detector has been applied to the newimage. Both faces have moved [311, 312] and are shown relative toprevious face regions [301, 302]. A third face region [303] has appearedand has been detected by the fast face detector [303]. In addition, afast face detector has found the smaller of the two previously confirmedfaces [304], because it is at the correct scale for the fast facedetector. Regions [303] and [304] are supplied as candidate regions[141] to the tracking module [111]. The tracking module merges this newcandidate region information [141], with the previous confirmed regioninformation [145] comprising regions [301] [302] to provide a set ofcandidate regions comprising regions [303], [304] and [302] to thecandidate region extractor [290]. The tracking module [111] knows thatthe region [302] has not been picked up by the detector [280]. This maybe because the face has either disappeared, remains at a size that wastoo large or small to be detected by the detector [280] or has changedsize to a size that the detector [280] was unable to detect. Thus, forthis region, the module [111] will preferably specify a large patch[305]. Referring to FIG. 3( c), this patch [305] is around the region[302] to be checked by the tracker [290]. Only the region [303] boundingthe newly detected face candidate will preferably be checked by thetracker [290], whereas because the face [301] is moving, a relativelylarge patch [306] surrounding this region is specified to the tracker[290].

FIG. 3( c) shows the situation after the candidate region extractoroperates upon the image. Candidate regions [306, 305] around both of theconfirmed face regions [301, 302] from the previous video frame as wellas new regions [303] are extracted from the full resolution image [130].The size of these candidate regions has been calculated by the facetracking module [111] based partly on statistical information relatingto the history of the current face candidate and partly on externalmetadata determined from other subsystems within the image acquisitionsystem. These extracted candidate regions are now passed on to thevariable sized face detector [121] which applies a VJ face detector tothe candidate region over a range of scales. The locations of anyconfirmed face regions are then passed back to the face tracking module[111].

FIG. 3( d) shows the situation after the face tracking module [111] hasmerged the results from both the fast face detector [280] and the facetracker [290] and applied various confirmation filters to the confirmedface regions. Three confirmed face regions have been detected [307, 308,309] within the patches [305,306,303] shown in FIG. 3( d). The largestregion [307] was known, but had moved from the previous video frame, andrelevant data is added to the history of that face region. Anotherpreviously known region [308] which had moved was also detected by thefast face detector which serves as a double-confirmation, and these dataare added to its history. Finally a new face region [303] was detectedand confirmed and a new face region history is then initiated for thisnewly detected face. These three face regions are used to provide a setof confirmed face regions [145] for the next cycle.

It will be seen that there are many possible applications for theregions 145 supplied by the face tracking module. For example, thebounding boxes for each of the regions [145] can be superimposed on thecamera display to indicate that the camera is automatically trackingdetected face(s) in a scene. This can be used for improving variouspre-capture parameters. One example is exposure, ensuring that the facesare well exposed. Another example is auto-focusing, by ensuring thatfocus is set on a detected face or indeed to adjust other capturesettings for the optimal representation of the face in an image.

The corrections may be done as part of pre-processing adjustments. Thelocation of the face tracking may also be used for post processing, andin particular selective post processing, where regions with faces may beenhanced. Such examples include sharpening, enhancing, saturating,brightening or increasing local contrast, or combinations thereof.Preprocessing using the locations of faces may also be used on regionswithout a face to reduce their visual importance, for example, throughselective blurring, desaturating, or darkening.

Where several face regions are being tracked, then the longest lived orlargest face can be used for focusing and can be highlighted as such.Also, the regions [145] can be used to limit areas on which, forexample, red-eye processing is performed (see, e.g., U.S. publishedpatent applications numbers 2004/0223063, 2005/0031224, 2005/0140801,and 2004/0041121, and U.S. Pat. Nos. 6,407,777 and 7,042,505, which arehereby incorporated by reference).

Other post-processing which can be used in conjunction with light-weightface detection is face recognition. In particular, such an approach canbe useful when combined with more robust face detection and recognitioneither running on the same device or an off-line device that hassufficient resources to run more resource-consuming algorithms

In this case, the face tracking module [111] reports the locations ofconfirmed face regions [145] to the in-camera firmware, preferablytogether with a confidence factor.

When the confidence factor is sufficiently high for a region, indicatingthat at least one face is in fact present in an image frame, the camerafirmware runs a light-weight face recognition algorithm [160] at thelocation of the face, for example a DCT-based algorithm. The facerecognition algorithm [160] uses a database [161] preferably stored onthe camera comprising personal identifiers and their associated faceparameters.

In operation, the module [160] collects identifiers over a series offrames. When the identifiers of a detected face tracked over a number ofpreview frames are predominantly of one particular person, that personis deemed by the recognition module to be present in the image. Theidentifier of the person, and the last known location of the face, arestored either in the image (in a header) or in a separate file stored onthe camera storage [150]. This storing of the person's ID can occur evenwhen a recognition module [160] fails for the immediately previousnumber of frames, but for which a face region was still detected andtracked by the module [111].

When the image is copied from camera storage to a display or permanentstorage device such as a PC (not shown), persons' ID's are copied alongwith the images. Such devices are generally more capable of running amore robust face detection and recognition algorithm and then combiningthe results with the recognition results from the camera, giving moreweight to recognition results from the robust face recognition (if any).The combined identification results are presented to the user, or ifidentification was not possible, the user is asked to enter the name ofthe person that was found. When the user rejects an identification or anew name is entered, the PC retrains its face print database anddownloads the appropriate changes to the capture device for storage inthe light-weight database [161].

When multiple confirmed face regions [145] are detected, the recognitionmodule [160] can detect and recognize multiple persons in the image.

It is possible to introduce a mode in the camera that does not take ashot until persons are recognized or until it is clear that persons arenot present in the face print database, or alternatively displays anappropriate indicator when the persons have been recognized. This allowsreliable identification of persons in the image.

This feature of a system in accordance with a preferred embodimentsolves a problem with algorithms that use a single image for facedetection and recognition and may have lower probability of performingcorrectly. In one example, for recognition, if a face is not alignedwithin certain strict limits it becomes very difficult to accuratelyrecognize a person. This method uses a series of preview frames for thispurpose as it can be expected that a reliable face recognition can bedone when many more variations of slightly different samples areavailable.

The present invention is not limited to the embodiments described aboveherein, which may be amended or modified without departing from thescope of the present invention as set forth in the appended claims, andstructural and functional equivalents thereof.

In methods that may be performed according to preferred embodimentsherein and that may have been described above and/or claimed below, theoperations have been described in selected typographical sequences.However, the sequences have been selected and so ordered fortypographical convenience and are not intended to imply any particularorder for performing the operations.

In addition, all references cited above herein, in addition to thebackground and summary of the invention sections themselves, are herebyincorporated by reference into the detailed description of the preferredembodiments as disclosing alternative embodiments and components.

What is claimed is:
 1. A method of detecting faces in an image stream,comprising: receiving an acquired image from said image stream includingone or more face regions; sub-sampling said acquired image at aspecified resolution to provide a sub-sampled image; identifying one ormore regions of said acquired image predominantly including skin tonesby applying one of a number of filters to define said one or moreregions of said acquired image predominantly including skin tones, atleast one of said filters including a restrictive skin filter and atleast one of said filters including a relaxed skin filter; calculating acorresponding integral image for at least one of said skin tone regionsof said sub-sampled acquired image; applying face detection to at leasta portion of said integral image to provide a set of one or morecandidate face regions each having a given size and a respectivelocation; providing a quality measure for one of a candidate region ofan image or said acquired image; and responsive to said quality measure,applying said relaxed skin filter to a subsequently acquired image.
 2. Amethod as claimed in claim 1, wherein responsive to failing to detect atleast one face region for said image, the method further comprisesenhancing the contrast of the luminance characteristics for at least aregion corresponding to one of said skin tone regions in a subsequentlyacquired image.
 3. A method as claimed in claim 1, wherein the applyingface detection comprises acquiring each of multiple new acquired imageswith progressively increased exposure parameters until at least onecandidate face region is detected.
 4. A method as claimed in claim 1,wherein the applying face detection comprises applying relaxed facedetection parameters.
 5. An iterative method of detecting faces in animage stream, the method comprising: receiving an acquired image fromsaid image stream including one or more face regions; sub-sampling saidacquired image at a specified resolution to provide a sub-sampled image;identifying one or more regions of said acquired image predominantlyincluding skin tones by applying one of a number of filters to definesaid one or more regions of said acquired image predominantly includingskin tones, at least one of said filters including a restrictive skinfilter and at least one of said filters including a relaxed skin filter;calculating a corresponding integral image for at least one of said skintone regions of said sub-sampled acquired image; and applying facedetection to at least a portion of said integral image to provide a setof one or more candidate face regions each having a given size and arespective location, wherein the applying face detection comprisesapplying relaxed face detection parameters.
 6. An iterative method asclaimed in claim 5, wherein responsive to failing to detect at least oneface region for said image, the method further comprises enhancing thecontrast of the luminance characteristics for at least a regioncorresponding to one of said skin tone regions in a subsequentlyacquired image.
 7. An iterative method as claimed in claim 5, whereinthe applying face detection comprises acquiring each of multiple newacquired images with progressively increased exposure parameters untilat least one candidate face region is detected.
 8. An image processingapparatus including one or more processors and one or more digitalstorage media having digitally-encoded instructions embedded therein forprogramming the one or more processors to perform an iterative method ofdetecting faces in an image stream, the method comprising: receiving anacquired image from said image stream including one or more faceregions; sub-sampling said acquired image at a specified resolution toprovide a sub-sampled image; identifying one or more regions of saidacquired image predominantly including skin tones by applying one of anumber of filters to define said one or more regions of said acquiredimage predominantly including skin tones, at least one of said filtersincluding a restrictive skin filter and at least one of said filtersincluding a relaxed skin filter; calculating a corresponding integralimage for at least one of said skin tone regions of said sub-sampledacquired image; applying face detection to at least a portion of saidintegral image to provide a set of one or more candidate face regionseach having a given size and a respective location; providing a qualitymeasure for one of a candidate region of an image or said acquiredimage; and responsive to said quality measure, applying said relaxedskin filter to a subsequently acquired image.
 9. An apparatus as claimedin claim 8, wherein responsive to failing to detect at least one faceregion for said image, the method further comprises enhancing thecontrast of the luminance characteristics for at least a regioncorresponding to one of said skin tone regions in a subsequentlyacquired image.
 10. An apparatus as claimed in claim 8, wherein theapplying face detection comprises acquiring each of multiple newacquired images with progressively increased exposure parameters untilat least one candidate face region is detected.
 11. An apparatus asclaimed in claim 8, wherein the applying face detection comprisesapplying relaxed face detection parameters.
 12. An image processingapparatus including one or more processors and one or more digitalstorage media having digitally-encoded instructions embedded therein forprogramming the one or more processors to perform an iterative method ofdetecting faces in an image stream, the method comprising: receiving anacquired image from said image stream including one or more faceregions; sub-sampling said acquired image at a specified resolution toprovide a sub-sampled image; identifying one or more regions of saidacquired image predominantly including skin tones by applying one of anumber of filters to define said one or more regions of said acquiredimage predominantly including skin tones, at least one of said filtersincluding a restrictive skin filter and at least one of said filtersincluding a relaxed skin filter; calculating a corresponding integralimage for at least one of said skin tone regions of said sub-sampledacquired image; and applying face detection to at least a portion ofsaid integral image to provide a set of one or more candidate faceregions each having a given size and a respective location, wherein theapplying face detection comprises applying relaxed face detectionparameters.
 13. An apparatus as claimed in claim 12, wherein responsiveto failing to detect at least one face region for said image, theiterative method further comprises enhancing the contrast of theluminance characteristics for at least a region corresponding to one ofsaid skin tone regions in a subsequently acquired image.
 14. Anapparatus as claimed in claim 12, wherein the applying face detectioncomprises acquiring each of multiple new acquired images withprogressively increased exposure parameters until at least one candidateface region is detected.
 15. One or more non-transitoryprocessor-readable digital storage media having digitally-encodedinstructions embedded therein for programming one or more processors toperform a method of detecting faces in an image stream, the methodcomprising: receiving an acquired image from said image stream includingone or more face regions; sub-sampling said acquired image at aspecified resolution to provide a sub-sampled image; identifying one ormore regions of said acquired image predominantly including skin tonesby applying one of a number of filters to define said one or moreregions of said acquired image predominantly including skin tones, atleast one of said filters including a restrictive skin filter and atleast one of said filters including a relaxed skin filter; calculating acorresponding integral image for at least one of said skin tone regionsof said sub-sampled acquired image; applying face detection to at leasta portion of said integral image to provide a set of one or morecandidate face regions each having a given size and a respectivelocation; providing a quality measure for one of a candidate region ofan image or said acquired image; and responsive to said quality measure,applying said relaxed skin filter to a subsequently acquired image. 16.A method as claimed in claim 15, wherein responsive to failing to detectat least one face region for said image, the method further comprisesenhancing the contrast of the luminance characteristics for at least aregion corresponding to one of said skin tone regions in a subsequentlyacquired image.
 17. A method as claimed in claim 15, wherein theapplying face detection comprises acquiring each of multiple newacquired images with progressively increased exposure parameters untilat least one candidate face region is detected.
 18. A method as claimedin claim 15, wherein the applying face detection comprises applyingrelaxed face detection parameters.
 19. An iterative method of detectingfaces in an image stream, the method comprising: receiving an acquiredimage from said image stream including one or more face regions;sub-sampling said acquired image at a specified resolution to provide asub-sampled image; identifying one or more regions of said acquiredimage predominantly including skin tones by applying one of a number offilters to define said one or more regions of said acquired imagepredominantly including skin tones, at least one of said filtersincluding a restrictive skin filter and at least one of said filtersincluding a relaxed skin filter; calculating a corresponding integralimage for at least one of said skin tone regions of said sub-sampledacquired image; and applying face detection to at least a portion ofsaid integral image to provide a set of one or more candidate faceregions each having a given size and a respective location, wherein theapplying face detection comprises applying relaxed face detectionparameters.
 20. An iterative method as claimed in claim 19, whereinresponsive to failing to detect at least one face region for said image,the method further comprises enhancing the contrast of the luminancecharacteristics for at least a region corresponding to one of said skintone regions in a subsequently acquired image.
 21. An iterative methodas claimed in claim 19, wherein the applying face detection comprisesacquiring each of multiple new acquired images with progressivelyincreased exposure parameters until at least one candidate face regionis detected.